Adaptive filter

ABSTRACT

In a method and filter and computer product for adaptive filtering of projection data acquired by a medical diagnostic apparatus, raw data-based filtering of the acquired projection data is undertaken using a filter with a filter kernel having a constant filter width, and the filtered projection data are mixed with the acquired projection data with a fixing of the respective quantitative relationships of filtered projection data to acquired projection data ensuing dependent on respective subsets of the acquired projection data.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present invention is directed to a method, apparatus andcomputer program product for adaptive filtering of projection dataacquired by means of a medical diagnosis apparatus.

[0003] 2. Description of the Prior Art

[0004] Projection data of an examined measurement subject can beacquired with modern medical diagnosis methods such as, for example,computed tomography. Generally, the examined measurement subject is apatient.

[0005] The acquired projection data usually are in digital form and arethus accessible to digital data processing. A digitization is initiallyneeded, given analog projection data.

[0006] Possible operations of the digital data processing are, forexample, an amplification, overlaying or filtering.

[0007] Since a number of manipulated variables can enter into theacquired projection data dependent on the measurement method and thediagnostic apparatus employed, the acquired projection data can comprisea plurality of dimensions.

[0008] In modern computed tomography (CT), the parameter 1 in themeasured data S (l, k, z) indicates the projection angle (the angularposition of the tube or, angular position of tube and detector system),k indicates the channel index (corresponding to the angle in the fan,given fan geometry and corresponding to the distance of the beam fromthe rotational center in parallel geometry), and z indicates the slice.

[0009] These acquired projection data (slice dataset) are thenreconstructed with a reconstruction method for planar data (usuallyfiltered back-projection or Fourier reconstruction) in order to obtainthe desired CT image.

[0010] In practically all medical diagnostic devices the image qualitywith reference to noise and low-contrast perceptibility increasesmonotonously with the patient dose. For improving the image quality,thus, an increase in the dose stress on the patient is generallyrequired. Such an increase in, the patient dose is possible only to alimited extent in order to avoid secondary harm.

[0011] Digital data processing offers an alternative possibility forreducing the pixel noise. For example, smoothing reconstruction filterscan be freely selected within certain limits without significanttechnical outlay in any commercially available CT apparatus, so that thenoise level in the image can be lowered by means of smoothing filteringwithout increasing the patient dose.

[0012] A disadvantage of smoothing reconstruction filters, however, isthat the entire dataset is smoothed with this method. This necessarilyleads to a degradation of the spatial resolution.

[0013] Approaches for adaptive filtering of the measured data are foundin the literature for solving this problem, i.e. the dataset is notglobally smoothed, but only locally smoothed (Jiang, “Adaptive trimmedmean for computer tomography image reconstruction, Proc. of SPIE, 2299,pp. 316-324, 1994; Jiang, “Adaptive filtering approach to the streakingartifact reduction due to x-ray photon starvation”, Radiology 205 (P),p. 391, 1997; Berkman Sahiner and Andrew E. Yagle, “Reconstruction fromprojections under timefrequency constraints”, IEEE Transactions onMedical Imaging, 14(2), pp. 193-204, 1995).

[0014] Usually, the acquired projection data of detector elementsneighboring in the k-direction are employed for the adaptive filtering.The filtering thus occurs exclusively in the l-direction.

[0015] German PS 198 53 143 also discloses a computed tomographyapparatus wherein the noise level of the interpolated projections doesnot exceed a specific threshold by means of 3D adaptive filtering bothin the channel-direction (ξ-coordinate), in projection direction(v-coordinate) as well as in the table feed direction (z-coordinate)according to the equation

ρ _(AF)(ξ,v,z)=∫dξ′dv′dz′∫Δξ(ξ−ξ′)∫Δv(v−v′)∫Δz(z−z′)ρ_(x)(ξ′,v′,z′).ρ_(x)(ξ,v,z)

[0016] denotes the projection data in parallel or fan geometry availablebefore the implementation of the adaptive filtering, P_(AF) (ξ,v,z)denotes the projection data in parallel or fan geometry available afterthe implementation of the adaptive filtering, and Δξ, Δv, Δz denote thefilter widths in the three coordinate directions.

[0017] These filter widths are a function of the projection valueρ_(x)(ξ, v, z) (adaptive filtering) to be currently filtered:Δξ=Δξ(ρ_(x)(ξ, v, z)), Δv=Δv(ρ_(x)(ξ, v, z)) and Δz=Δz(ρ_(x)(ξ,v, z)).fΔξ(·), fΔv(·) and fΔz(·) reference the filter function (axiallysymmetrical with values ≧0 and total area 1) for the smoothing in therespective coordinates. The filter widths Δξ, Δv, and Δz respectivelyrepresent the half intensity widths or some other characteristic widthcriterion of the filter functions. When one or more of the widths is/arezero, then the filter function is reduced to a Dirac delta function andno filtering occurs in the corresponding coordinates.

[0018] German PS 198 53 143 thus discloses a method for filteringmulti-dimensional planar projection data (attenuation values) of a CTscan wherein the adaptation of the filter to the projection data underconsideration ensues by variation of the width of the filter kernel(filter width) in the individual dimensions.

[0019] A disadvantage of this known method is that the implementation ofthe method requires considerable computing and time expenditure due tothe adaptively fluctuating filter width in the individual dimensions.

SUMMARY OF THE INVENTION

[0020] An object of the present invention is to provide a method foradaptive filtering of projection data acquired by means of a medicaldiagnostic apparatus wherein noise in the acquired projection data canbe designationally reduced with only little computing outlay, and toprovide a computed tomography apparatus operating according to themethod.

[0021] This object is achieved according to the invention in a methodfor adaptive filtering of projection data acquired by a medicaldiagnostic apparatus, and a medical apparatus for implementing themethod, wherein raw data-based filtering of the acquired projection datais undertaken using a filter with a filter kernel having a constantfilter width, and the filtered projection data are mixed with theacquired projection data, with a fixing of the respective quantitativerelationships of the filtered projection data to the acquired projectiondata ensuing dependent on at least a subset of the acquired projectiondata.

[0022] Since the filter width of the filter kernel is constant in theinvention, the method can be implemented without high calculatingoutlay. The time required for an adaptive filtering of the projectiondata acquired by means of a medical diagnostic apparatus thus can bereduced. Also contributing to this is that the mixing function formixing the filtered measured data with the unfiltered measured data isone-dimensional, i.e. the same for all dimensions.

[0023] As used herein “raw data-based filtering” means that the actualprojection data are filtered before the reconstruction of an image hasoccurred. Accordingly, the projection data in the form of raw data donot yet represent an image. In order to be able to display an image, animage reconstruction must first occur, for example on the basis of thestandard method of convoluted back-projection.

[0024] In a preferred embodiment, the projection data have two or moredimensions, and the filtering of the projection data ensues in alldimensions with a filter having a two-dimensional or multi-dimensionalfilter kernel, with the filter width in each individual dimension beingconstant.

[0025] Due to the two or more-dimensional design of the filter kernel,it is possible—given a constant filter width—to increase the quantumaveraging and to thus reduce the noise further.

[0026] The mixing of the filtered projection data with the unfilteredprojection data can ensue especially simply when respective adaptationfactors dependent on at least a subset of the acquired projection dataare defined for the acquired projection data and for the filteredprojection data, and the respective quantitative relationships offiltered projection data to acquired projection data in the mixing arefixed by the respective adaptation factors.

[0027] For simplifying the inventive method, it can be advantageous wheneither the adaptation factor for the acquired projection data or theadaptation factor for the filtered projection data is equal to one.

[0028] In another preferred embodiment of the inventive method, athreshold for the acquired projection data is defined in an additionalstep, and at least one adaptation factor is defined dependent on adifference between the acquired projection data and the threshold.

[0029] Using the adaptation factor defined in this way, it is possiblein an especially simple way to except areas of the acquired projectiondata from the adaptive filtering by means of a suitable selection of thethreshold.

[0030] This can ensue, for example, by setting the respective adaptationfactor for the acquired projection data equal to one and setting therespective adaptation factor for the filtered projection data equal tozero when the difference between acquired projection data and thresholdis less than or equal to zero.

[0031] In another embodiment of the inventive method, the adaptationfactor for the filtered projection data is determined dependent on adifference between the acquired projection data and the threshold, andthe adaptation factor for the acquired projection data is defined by thedifference between one and the adaptation factor for the filteredprojection data when the difference between acquired projection data andthreshold is greater than zero.

[0032] As a result of this procedure, the adaptation factor for thefiltered projection data and the adaptation factor for the unfiltered,acquired projection data always supplement one another to form the valueof one, which generally corresponds to 100%. This allows an especiallysimple controllability of the inventive method.

[0033] The filter for projection data with three dimensions according toa first preferred embodiment of the present invention, has the form

S _(af)(l, k, m)=g(S)·S _(f)(l, k, m)+h(S)·S(l, k, m).

[0034] The variables l, k, m are the dimensions of the projection data,S(l, k, m) is the unfiltered three-dimensional projection data, g(S),h(S) are adaptation factors dependent on the respective projection datato be filtered, S_(f)(l, k, m) is the projection data filtered with afilter having three-dimensional filter kernel, and S_(af)(l, k, m) isthe adaptively filtered projection data, as the output quantity of thefilter.

[0035] According to a second preferred embodiment of the inventivemethod, the filter for projection data with three dimensions has theform

S _(af)(l, k, m)=S(l, k, m) for S(l, k, m)−SW≦0

and

S _(af)(l, k, m)=A(S−SW)·S _(f)(l, k, m)+(1−A(S−SW))·S(l, k, m) for S(l,k, m)−SW>0.

[0036] The variables l, k, m are the dimensions of the projection data,S(l, k, m) is the unfiltered three-dimensional projection data, SW is anadjustable threshold, A(S−SW) is an adaptive adaptation factor, i.e. anadaptation factor dependent on the respective projection data to befiltered, S_(f)(l, k, m) is the projection data filtered with a filterhaving three-dimensional filter kernel, and S_(af)(l, k, m) is theadaptively filtered projection data as output quantity of the filter.A(S−SW) becomes greater the more highly the projection data S(l, k, m)exceed the threshold SW.

[0037] It is especially advantageous when the adaptation factor isselected such that the signal noise of a signal voltage of theadaptively filtered projection data remains constant independently ofthe acquired projection data.

[0038] The projection data filtered with a filter having amulti-dimensional filter kernel preferably are presented according tothe present invention in the form${{S_{f}\left( {l,k,m} \right)} = {\sum\limits_{k^{\prime},m^{\prime}}{G_{l^{\prime},k^{\prime},m^{\prime}} \cdot S_{{l - l^{\prime}},{k - k^{\prime}},{m\quad - m^{\prime}}}}}},$

[0039] wherein G_(l′,k′,m′) is the filter kernel and S_(l-l′,k-k′,m-m′)references the unfiltered projection data.

[0040] The inventive method can be especially advantageously utilizedwhen the attenuation values of a computer tomograph are employed as theprojection data.

[0041] An apparatus for the implementation of the inventive method has afilter with a filter kernel having a constant filter width that issuitable for raw data-based filtering of the acquired projection, and amixer device for mixing the filtered projection data with the acquiredprojection data, the mixer device being suitable for implementing adefinition of the respective quantitative relationships of the filteredprojection data to the acquired projection data dependent on at least asubset of the acquired projection data.

[0042] The present invention is also directed to a computer programproduct that is suitable for implementing a method as described abovewhen loaded in a memory of a processing device.

DESCRIPTION OF THE DRAWINGS

[0043]FIG. 1 schematically illustrates the sequence of the inventivemethod in a first embodiment.

[0044]FIG. 2 schematically illustrates the sequence of the inventivemethod in a second embodiment.

[0045]FIG. 3 illustrates an apparatus that is suitable for theimplementation of the inventive method.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0046] The inventive apparatus shown in FIG. 3 suitable for theimplementation of the inventive method shall be described below first.

[0047] The inventive apparatus 5 for the implementation of the inventivemethod is connected to a medical device 1, for example a computedtomography apparatus, and has a filter 2 with a filter kernel ofconstant filter width that is suited for raw data-based filtering of theacquired projection data.

[0048] The filtering ensues for the purpose of reducing a pixel noisethat is always present because of noise quantities in the acquiredprojection data. Since the acquired projection data are filteredoverall, the desired reduction of the pixel noise is accompanied,however, by a disadvantageous, degraded spatial resolution in thefiltered projection data.

[0049] The filter 2 is connected to a mixer device 3 that is likewisepart of the inventive apparatus 5.

[0050] The mixer device 3 is suited for mixing the filtered projectiondata with the acquired projection data, with a definition of therespective, quantitative relationships of filtered projection data toacquired projection data ensuing dependent on the respective subset ofacquired projection data under consideration, i.e. adaptively.

[0051] Adaptively filtered projection data are thus made available atthe output of the mixer device 3.

[0052] The result of this adaptive mixing of the filtered projectiondata to the acquired, i.e. unfiltered projection data for the purpose ofgenerating adaptively filtered projection data is that the pixel noisein the projection data can be designationally reduced without having toaccept a global degradation of the spatial resolution.

[0053] The reduction of the pixel noise thus can be attributed to theproportion of filtered projection data, and the retention of the topicalresolution can be attributed to the proportion of acquired projectiondata.

[0054] The weighting of filtered projection data to acquired projectiondata is therefore set dependent on a subset of the acquired projectiondata so that an adequate reduction of the pixel noise is achieved in theadaptively filtered projection data, but with retention of an adequatespatial resolution.

[0055] The adaptively filtered projection data acquired in this way canbe supplied as an output to a user, usually a physician, in the form ofa CT image via an output device 4.

[0056] Alternatively to the embodiment shown in FIG. 3, it should benoted that the filter 2 and the mixer device 3 can form a fixed unit.For example, it would be possible to realize the filter 2 as well as themixer device 3 in a computer chip (not shown).

[0057]FIG. 1 schematically shows the sequence of the inventive methodaccording to a first preferred embodiment as can be realized, forexample, in the above-described, inventive apparatus 5.

[0058] In this first preferred embodiment, projection data S(l, k, m)are acquired with the medical device 1 in a first step S11.Alternatively, the projection data S(l, k, m) can have already beenobtained and stored in a memory device (not shown) and read outtherefrom in step S11.

[0059] These acquired projection data S(l, k, m) can, for example, bethe attenuation values of a computed tomography apparatus.

[0060] In the embodiment shown in FIG. 1, the projection data have threedimensions l, k, m. Alternatively, however, the employment ofn-dimensional projection data is generally possible (with n≧1).

[0061] In step S12, the acquired projection data S(l, k, m) are filteredin the filter 2 using a filter kernel having a constant filter width.Since the projection data S(l, k, m) in the embodiment shown in FIG. 1have three dimensions l, k, m, the filtering also ensues with athree-dimensional filter kernel in all dimensions l, k, m, with thefilter width being constant in each of the individual dimensions l, k m.

[0062] Due to the three-dimensional design of the filter kernel andcompared to a one-dimensional design of the filter kernel that islikewise inventively possible, the quantum averaging is increased givena constant filter width, and thus noise in each dimension of theprojection data is designationally reduced further.

[0063] Given n-dimensional projection data, thus, the filteringgenerally ensues in all n dimensions with a filter having ann-dimensional filter kernel, with the respective filter widths in theindividual dimensions being constant.

[0064] Filtered projection data S_(f)(l, k, m) are obtained as outputsignal of step S12.

[0065] In the steps S13 and S14, which are parallel to one another, theacquired projection data S_(f)(l, k, m) are respectively weighted withan adaptation factor g(S) and h(S).This weighting can ensue with aweighting device (not shown) that can be part of the inventive apparatus5. The weighting device preferably is integrated into the mixer device3.

[0066] The weighting preferably ensues in each dimension l, k, m anddependent on at least one subset of the acquired projection data S(l, k,m), i.e. adaptively. Adaptation factors are thereby one-dimensional,i.e. the same for each dimension.

[0067] Alternatively, it is also possible to define only one adaptationfactor and set the other adaptation factor equal to one.

[0068] In the following step S15, the filtered projection data S_(f)(l,k, m) are mixed with the acquired projection data S(l, k, m) by themixer device 3. Due to the adaptive weighting of the filtered projectiondata S_(f)(l, k, m) and the unfiltered projection data S(l, k, m) withthe adaptation factors g(S), h(S), the definition of the respectivequantitative relationship of filtered projection data S_(f)(l, k, m) tounfiltered projection data S(l, k, m) also ensues adaptively, i.e.dependent on respective subsets of the acquired projection data S(l, k,m).

[0069] In the first embodiment, the acquired, adaptively filteredprojection data S_(af)(l, k, m) thus can be written in the form

S _(af)(l, k, m)=g(S)·S _(f)(l, k, m)+h(S)·S(l, k, m).

[0070] With the inventive method and also without employing a filterwith adaptive filter width, it is consequently possible todesignationally reduce the noise in a respective dimension of theprojection data while avoiding an increased computing time expenditure.

[0071] In the embodiment of FIG. 2, analogous to the above-described,first embodiment, projection data S(l, k, m) are also acquired first instep S21 by the medical device 1, said projection data S(l, k, m) beingfiltered in a further step S23 upon employment of a filter 2 with afilter kernel having a constant filter width in order to obtain filteredprojection data S_(f)(l, k, m).

[0072] Parallel thereto, a threshold SW for the acquired projection dataS(l, k, m) is defined in step S22 in a threshold-setting device (notshown in FIG. 3) that can be part of the inventive apparatus 5.

[0073] In step S24, a check is carried out a testing device (not shownin FIG. 3) to determine whether the difference of the acquiredprojection data S(l, k, m) minus the threshold SW is less than or equalto “0”:S(l, k, m)−SW≦0. The testing device also can be part of theinventive apparatus 5, whereby it is preferably integrated into themixer device 3 and is connected to the threshold-setting device.

[0074] When step S24 supplies the value “true” (“yes”) as result, thenthe filtered projection data S_(f)(l, k, m) are weighted with “0” in themixer device (the adaptation factor for the filtered projection data is“0”) and thus do not enter into the final result. The acquiredprojection data S(l, k, m), in contrast, are weighted with “1” accordingto this embodiment.

[0075] Consequently, the adaptively filtered projection data S_(af) atthe output of the mixer device 3 derive as S_(af)(l, k, m)=S(l, k, m)for S(l, k, m)−SW≦0 according to this embodiment.

[0076] When the step S24 supplies the value “false” (no”) as result,then the filtered projection data S_(f)(l, k, m) are weighted by themixer device 3 dependent on a difference between the acquired projectiondata S(l, k, m) and the threshold SW. The adaptation factor for thefiltered projection data S_(f)(l, k, m) can thus be written in the formA(S−SW). A(S−SW) thereby increases the more highly the projection data Sexceed the threshold SW.

[0077] At the same time, the acquired projection data S(l, k, m) areweighted such by the mixer device 3 that the sum of the weighting of thefiltered projection data S_(f)(l, k, m) and the weighting of theunfiltered projection data S(l, m, k) yields 1 or 100%.

[0078] The adaptation factor for the acquired projection data can thusbe presented in the form 1−A(S−SW).

[0079] Consequently, the adaptively filtered projection data S_(af) atthe output of the mixer device derive as

S _(af)(l, k, m)=A(S−SW)·S _(f)(l, k, m)+(1−A(S−SW))·S(l, k, m)

[0080] for S(l, k, m)−SW>0 according to this embodiment.

[0081] Without employing a filter with adaptive filter width, the secondembodiment of the inventive method described in the above example alsomakes it possible to designationally reduce the noise in the projectiondata while avoiding an increased calculating time expenditure.

[0082] According to another, preferred embodiment of the presentinvention, the inventive method is implemented in a computer programproduct that can be loaded into a processing device (for example, acomputer or the above-described apparatus 5 of the invention) for theimplementation of the inventive method.

[0083] Although modifications and changes may be suggested by thoseskilled in the art, it is the intention of the inventors to embodywithin the patent warranted hereon all changes and modifications asreasonably and properly come within the scope of their contribution tothe art.

We claim as our invention:
 1. A method for adaptively filteringprojection data acquired by a medical diagnostic apparatus, comprisingthe steps of: filtering said acquired projection data, to obtainfiltered projection data, in a filter having a filter kernel with aconstant filter width, and preserving said acquired projection data asunfiltered projection data; and mixing said filtered projection datawith said unfiltered projection data, with respective quantitativerelationships of the filtered projection data to the unfilteredprojection data, and fixing said respective quantitative relationshipsdependent on at least a subset of said unfiltered projection data.
 2. Amethod as claimed in claim 1 wherein said projection data have at leasttwo dimensions, and wherein the step of filtering said projection datacomprises filtering said projection data in all of said dimensions witha filter having an at least two-dimensional filter kernel, with a filterwidth in each of said at least two dimensions being constant.
 3. Amethod as claimed in claim 1 comprising defining respective adaptationfactors for said unfiltered projection data and said filtered projectiondata dependent on said subset, and fixing said respective quantitativerelationships dependent on the respective adaptation factors.
 4. Amethod as claimed in claim 3 comprising setting the adaptation factorfor the unfiltered projection data or the adaptation factor for thefiltered projection data equal to one.
 5. A method as claimed in claim 4comprising the additional step of defining a threshold for saidunfiltered projection data, and setting at least one of said respectiveadaptation factors dependent on a difference between said unfilteredprojection data and said threshold.
 6. A method as claimed in claim 4comprising setting the respective adaptation factor for said unfilteredprojection data equal to one, and setting the respective adaptationfactor for the filtered projection data equal to zero when saiddifference between said unfiltered projection data and said threshold isless than or equal to zero.
 7. A method as claimed in claim 6 comprisingsetting the respective adaptation filter for said filtered projectiondata dependent on said difference between said unfiltered projection andsaid threshold and setting the adaptation factor for the unfilteredprojection data as a difference between one and said adaptation factorfor the filtered projection data, when said difference between saidunfiltered projection data and said threshold is greater than zero.
 8. Amethod as claimed in claim 3 wherein said projection data have threedimensions, and filtering said projection data with a filter having aform S _(af)(l, k, m)=g(S)·S _(f)(l, k, m)+h(S)·S(l, k, m), wherein l,k, m denote the dimensions of the projection data, S(l, k, m) denotesthe unfiltered three-dimensional projection data, g(S), h(S) denotesadaptation factors dependent on the respective projection data to befiltered, S_(f)(l, k, m) denotes the projection data filtered with afilter having three-dimensional filter kernel, and S_(af)(l, k, m)denotes the adaptively filtered projection data as output quantity ofthe filter.
 9. A method as claimed in claim 8 comprising setting saidadaptation factor so that a signal noise of a signal voltage of saidfiltered projection data S_(af)(l,k,m) remains constant independently ofsaid unfiltered projection data S(l,k,m).
 10. A method as claimed inclaim 8 wherein said projection data filtered with said filter havingsaid three-dimensional filter kernel have a form${{S_{f}\left( {l,k,m} \right)} = {\sum\limits_{k^{\prime},m^{\prime}}{G_{l^{\prime},k^{\prime},m^{\prime}} \cdot S_{{l - l^{\prime}},{k - k^{\prime}},{m\quad - m^{\prime}}}}}},$

wherein G_(l′,k′,m′) denotes the filter kernel and S_(l-l′,k-k′,m-m′)references the unfiltered projection data.
 11. A method as claimed inclaim 3 wherein said projection data have three dimensions, andcomprising the additional steps of defining a threshold for saidunfiltered projection data, defining an adaptation factor dependent on adifference between the unfiltered projection data and said threshold,and filtering said three-dimensional projection data in a filter havinga three-dimensional filter kernel with a form S _(af)(l, k, m)=S(l, k,m) for S(l, k, m)−SW≦0 and S _(af)(l, k, m)=A(S−SW)·S _(f)(l, k,m)+(1−A(S−SW))·S(l, k, m) for S(l, k, m)−SW>0 wherein l, k, m denote thedimensions of the projection data, S(l, k, m) denotes the unfilteredthree-dimensional projection data, SW denotes an adjustable threshold,A(S−SW) denotes said adaptation factor, S_(f)(l, k, m) denotes theprojection data filtered with said filter having a three-dimensionalfilter kernel, and S_(af)(l, k, m) denotes the adaptively filteredprojection data as an output quantity of the filter.
 12. A method asclaimed in claim 11 comprising setting said adaptation factor so that asignal noise of a signal voltage of said filtered projection dataS_(af)(l,k,m) remains constant independently of said unfilteredprojection data S(l,k,m).
 13. A method as claimed in claim 11 whereinsaid projection data filtered with said filter having saidthree-dimensional filter kernel have a form
 14. A method as claimed inclaim 1 comprising employing attenuation values obtained in a computedtomography apparatus as said projection data.
 15. An apparatus foradaptively filtering projection data acquired by a medical diagnosticapparatus, comprising the steps of: an input for receiving project dataacquired by a medical diagnostic apparatus; a filter connected to saidinput for filtering said acquired projection data, to obtain filteredprojection data, said filter having a filter kernel with a constantfilter width; and a mixer connected to an output of said filter and tosaid input for mixing said filtered projection data with said acquiredprojection data, as unfiltered projection data, with respectivequantitative relationships of the filtered projection data to theunfiltered projection data, and fixing said respective quantitativerelationships dependent on at least a subset of said unfilteredprojection data.
 16. An apparatus method as claimed in claim 15 whereinsaid projection data have at least two dimensions, and wherein saidfilter filters said projection data in all of said dimensions with an atleast two-dimensional filter kernel, with a filter width in each of saidat least two dimensions being constant.
 17. An apparatus as claimed inclaim 15 wherein said mixer defines respective adaptation factors forsaid unfiltered projection data and said filtered projection datadependent on said subset, and fixes said respective quantitativerelationships dependent on the respective adaptation factors.
 18. Anapparatus as claimed in claim 17 wherein said mixer sets the adaptationfactor for the unfiltered projection data or the adaptation factor forthe filtered projection data equal to one.
 19. An apparatus as claimedin claim 17 wherein said mixer defines a threshold for said unfilteredprojection data, and sets at least one of said respective adaptationfactors dependent on a difference between said unfiltered projectiondata and said threshold.
 20. An apparatus as claimed in claim 19 whereinsaid mixer sets the respective adaptation factor for said unfilteredprojection data equal to one, and sets the respective adaptation factorfor the filtered projection data equal to zero when said differencebetween said unfiltered projection data and said threshold is less thanor equal to zero.
 21. An apparatus as claimed in claim 19 wherein saidmixer sets the respective adaptation filter for said filtered projectiondata dependent on said difference between said unfiltered projection andsaid threshold and sets the adaptation factor for the unfilteredprojection data as a difference between one and said adaptation factorfor the filtered projection data, when said difference between saidunfiltered projection data and said threshold is greater than zero. 22.An apparatus as claimed in claim 17 wherein said projection data havethree dimensions, and wherein said filter has a form S _(af)(l, k,m)=g(S)·S _(f)(l, k, m)+h(S)·S(l, k, m), wherein l, k, m denote thedimensions of the projection data, S(l, k, m) denotes the unfilteredthree-dimensional projection data, g(S), h(S) denotes adaptation factorsdependent on the respective projection data to be filtered, S_(f)(l, k,m) denotes the projection data filtered with a filter havingthree-dimensional filter kernel, and S_(af)(l, k, m) denotes theadaptively filtered projection data as output quantity of the filter.23. An apparatus as claimed in claim 22 wherein said mixer sets saidadaptation factor so that a signal noise of a signal voltage of saidfiltered projection data S_(af)(l,k,m) remains constant independently ofsaid unfiltered projection data S(l,k,m).
 24. An apparatus as claimed inclaim 22 wherein said projection data filtered with said filter havingsaid three-dimensional filter kernel have a form${{S_{f}\left( {l,k,m} \right)} = {\sum\limits_{k^{\prime},m^{\prime}}{G_{l^{\prime},k^{\prime},m^{\prime}} \cdot S_{{l - l^{\prime}},{k - k^{\prime}},{m\quad - m^{\prime}}}}}},$

wherein G_(l′,k′,m′) denotes the filter kernel and S_(l-l′,k-k′,m-m′)references the unfiltered projection data.
 25. An apparatus as claimedin claim 17 wherein said projection data have three dimensions, andwherein said mixer defines a threshold for said unfiltered projectiondata, defines an adaptation factor dependent on a difference between theunfiltered projection data and said threshold, and wherein said filterhas a three-dimensional filter kernel with a form S _(af)(l, k, m)=S(l,k, m) for S(l, k, m)−SW≦0 and S _(af)(l, k, m)=A(S−SW)·S _(f)(l, k,m)+(1−A(S−SW))·S(l, k, m) for S(l, k, m)−SW≧0 wherein l, k, m denote thedimensions of the projection data, S(l, k, m) denotes the unfilteredthree-dimensional projection data, SW denotes an adjustable threshold,A(S−SW) denotes said adaptation factor, S_(f)(l, k, m) denotes theprojection data filtered with said filter having a three-dimensionalfilter kernel, and S_(af)(l, k, m) denotes the adaptively filteredprojection data as an output quantity of the filter.
 26. An apparatus asclaimed in claim 25 wherein said mixer sets said adaptation factor sothat a signal noise of a signal voltage of said filtered projection dataS_(af)(l,k,m) remains constant independently of said unfilteredprojection data S(l,k,m).
 27. An apparatus as claimed in claim 25wherein said projection data filtered with said filter having saidthree-dimensional filter kernel have a form${{S_{f}\left( {l,k,m} \right)} = {\sum\limits_{k^{\prime},m^{\prime}}{G_{l^{\prime},k^{\prime},m^{\prime}} \cdot S_{{l - l^{\prime}},{k - k^{\prime}},{m\quad - m^{\prime}}}}}},$

wherein G_(l′,k′,m′) denotes the filter kernel and S_(l-l′,k-k′,m-m′)references the unfiltered projection data.
 28. An apparatus as claimedin claim 15 wherein said input received attenuation values obtained in acomputed tomography apparatus as said acquired projection data.
 29. Acomputer program loadable into a processor for adaptively filteringprojection data acquired by a medical diagnostic apparatus, causing saidprocessor to: filter said acquired projection data, to obtain filteredprojection data, in a filter having a filter kernel with a constantfilter width, and preserve said acquired projection data as unfilteredprojection data; and mix said filtered projection data with saidunfiltered projection data, with respective quantitative relationshipsof the filtered projection data to the unfiltered projection data, andfixing said respective quantitative relationships dependent on at leasta subset of said unfiltered projection data.
 30. A computer program asclaimed in claim 29 wherein said projection data have at least twodimensions, and which causes said processor to filter said projectiondata in all of said dimensions with a filter having an at leasttwo-dimensional filter kernel, with a filter width in each of said atleast two dimensions being constant.
 31. A computer program as claimedin claim 29 which causes said processor to define respective adaptationfactors for said unfiltered projection data and said filtered projectiondata dependent on said subset, and to fix said respective quantitativerelationships dependent on the respective adaptation factors.
 32. Acomputer program as claimed in claim 31 which causes said processor toset the adaptation factor for the unfiltered projection data or theadaptation factor for the filtered projection data equal to one.
 33. Acomputer program as claimed in claim 31 which causes said processor todefine a threshold for said unfiltered projection data, and setting atleast one of said respective adaptation factors dependent on adifference between said unfiltered projection data and said threshold.34. A computer program as claimed in claim 33 which causes saidprocessor to set the respective adaptation factor for said unfilteredprojection data equal to one, and setting the respective adaptationfactor for the filtered projection data equal to zero when saiddifference between said unfiltered projection data and said threshold isless than or equal to zero.
 35. A computer program as claimed in claim34 which causes said processor to set the respective adaptation filterfor said filtered projection data dependent on said difference betweensaid unfiltered projection and said threshold and to set the adaptationfactor for the unfiltered projection data as a difference between oneand said adaptation factor for the filtered projection data, when saiddifference between said unfiltered projection data and said threshold isgreater than zero.
 36. A computer program as claimed in claim 31 whereinsaid projection data have three dimensions, and wherein said computerprogram causes said processor to filter said projection data with afilter having a form S _(af)(l, k, m)=g(S)·S _(f)(l, k, m)+h(S)·S(l, k,m), wherein l, k, m denote the dimensions of the projection data, S(l,k, m) denotes the unfiltered three-dimensional projection data, g(S),h(S) denotes adaptation factors dependent on the respective projectiondata to be filtered, S_(f)(l, k, m) denotes the projection data filteredwith a filter having three-dimensional filter kernel, and S_(af)(l, k,m) denotes the adaptively filtered projection data as output quantity ofthe filter.
 37. A computer program as claimed in claim 36 which causessaid processor to set said adaptation factor so that a signal noise of asignal voltage of said filtered projection data S_(af)(l,k,m) remainsconstant independently of said unfiltered projection data S(l,k,m). 38.A computer program as claimed in claim 36 wherein said projection datafiltered with said filter having said three-dimensional filter kernelhave a form${{S_{f}\left( {l,k,m} \right)} = {\sum\limits_{k^{\prime},m^{\prime}}{G_{l^{\prime},k^{\prime},m^{\prime}} \cdot S_{{l - l^{\prime}},{k - k^{\prime}},{m\quad - m^{\prime}}}}}},$

wherein G_(l′,k′,m′) denotes the filter kernel and S_(l-l′,k-k′,m-m′)references the unfiltered projection data.
 39. A computer program asclaimed in claim 31 wherein said projection data have three dimensions,and wherein said computer program causes said processor to define athreshold for said unfiltered projection data, to define an adaptationfactor dependent on a difference between the unfiltered projection dataand said threshold, and to filter said three-dimensional projection datain a filter having a three-dimensional filter kernel with a form S_(af)(l, k, m)=S(l, k, m) for S(l, k, m)−SW≦0 and S _(af)(l, k,m)=A(S−SW)·S _(f)(l, k, m)+(1−A(S−SW))·S(l, k, m) for S(l, k, m)−SW>0wherein l, k, m denote the dimensions of the projection data, S(l, k, m)denotes the unfiltered three-dimensional projection data, SW denotes anadjustable threshold, A(S−SW) denotes said adaptation factor, S_(f)(l,k, m) denotes the projection data filtered with said filter having athree-dimensional filter kernel, and S_(af)(l, k, m) denotes theadaptively filtered projection data as an output quantity of the filter.40. A computer program as claimed in claim 39 which causes saidprocessor to set said adaptation factor so that a signal noise of asignal voltage of said filtered projection data S_(af)(l,k,m) remainsconstant independently of said unfiltered projection data S(l,k,m). 41.A computer program as claimed in claim 39 wherein said projection datafiltered with said filter having said three-dimensional filter kernelhave a form${{S_{f}\left( {l,k,m} \right)} = {\sum\limits_{k^{\prime},m^{\prime}}{G_{l^{\prime},k^{\prime},m^{\prime}} \cdot S_{{l - l^{\prime}},{k - k^{\prime}},{m\quad - m^{\prime}}}}}},$

wherein G_(l′,k′,m′) denotes the filter kernel and S_(l-l′,k-k′,m-m′)references the unfiltered projection data.
 42. A computer program asclaimed in claim 29 which causes said processor to operate onattenuation values obtained in a computed tomography apparatus as saidprojection data.